| dc.contributor.advisor | 翁久幸 | zh_TW |
| dc.contributor.author (Authors) | 葉秉鈞 | zh_TW |
| dc.contributor.author (Authors) | Yeh, Ping-Chun | en_US |
| dc.creator (作者) | 葉秉鈞 | zh_TW |
| dc.creator (作者) | Yeh, Ping-Chun | en_US |
| dc.date (日期) | 2023 | en_US |
| dc.date.accessioned | 2-Aug-2023 13:05:17 (UTC+8) | - |
| dc.date.available | 2-Aug-2023 13:05:17 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-Aug-2023 13:05:17 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0110354027 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/146311 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計學系 | zh_TW |
| dc.description (描述) | 110354027 | zh_TW |
| dc.description.abstract (摘要) | 馬可夫鏈蒙地卡羅法（Markov chain Monte Carlo，MCMC）在貝氏統計中是一種隨機化方法，在處理複雜函數時表現較好，但需要較長時間收斂。相對而言，貝氏統計中的拉普拉斯近似法較為快速，雖然對於後驗分配的估計較不精準，但在處理大規模資料上，使用這些方法仍是可考慮的選項。關於顧客購買時間間隔的資料，過去的研究或使用最大概似估計，或使用層級貝氏模型並以MCMC方法進行估計。前者雖計算速度較快，但有過度擬合（overfitting）的情形。後者雖準確度較高，但以MCMC方法運算費時，在大規模資料較不適用。本論文考慮層級貝氏模型以避免過度擬合，再以最大後驗估計取代MCMC之估計，來處理較大比數的資料並提升預測率。總結而言，本研究結合層級貝氏模型與最大後驗估計，應用於顧客購買時間間隔的問題。本研究的結果顯示，最大後驗估計相較於最大概似估計，雖計算時間稍長，但在AUC有得到提升；而最大後驗估計在計算量上遠低於MCMC，故較適合應用於大規模資料。 | zh_TW |
| dc.description.abstract (摘要) | Markov chain Monte Carlo (MCMC) is a stochastic method in Bayesian statistics that performs well when dealing with complex functions but requires a longer convergence time. In contrast, several approximate methods in Bayesian statistics are faster but may have lower accuracy. However, these methods can still be considered when handling large-scale data. In previous studies on customers’ interpurchase times , researchers have either used maximum likelihood estimation or employed hierarchical Bayesian models with MCMC for estimation. The former method is faster in computation but prone to overfitting. The latter method offers higher accuracy but is time-consuming when using MCMC, making it less suitable for large-scale data. This paper considers hierarchical Bayesian models to avoid overfitting and replaces MCMC estimation with maximum a posteriori estimation to handle larger datasets and improve prediction accuracy. In summary, this study combines hierarchical Bayesian models with maximum a posteriori estimation for the problem of customers’ interpurchase times. The results show that, compared to maximum likelihood estimation, maximum a posteriori estimation has slightly longer computation time but yields improvements in AUC estimation. On the other hand, maximum a posteriori estimation has significantly lower computational requirements than MCMC, making it more suitable for large-scale data applications. | en_US |
| dc.description.tableofcontents | 摘要 2ABSTRACT 3第一章 緒論與研究動機 6第二章 文獻回顧 7第一節 廣義伽瑪分配-機率密度函數與危險函數 7第二節 廣義伽瑪分配-參數估計方法 8第三節 層級貝氏模型 11第三章 研究方法 12第一節 常用正規化方法介紹 12第二節 MCMC方法介紹 15第三節 層級貝氏模型搭配最大後驗估計法 16第四章 研究結果 19第一節 資料說明 19第二節 層級貝氏法搭配最大後驗估計之結果 20第五章 結論與建議 33參考文獻 34 | zh_TW |
| dc.format.extent | 1811796 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0110354027 | en_US |
| dc.subject (關鍵詞) | 購買間隔時間 | zh_TW |
| dc.subject (關鍵詞) | 廣義伽瑪分配 | zh_TW |
| dc.subject (關鍵詞) | 最大後驗估計 | zh_TW |
| dc.subject (關鍵詞) | 拉普拉斯近似 | zh_TW |
| dc.subject (關鍵詞) | 正規化 | zh_TW |
| dc.subject (關鍵詞) | interpurchase times | en_US |
| dc.subject (關鍵詞) | generalized gamma distribution | en_US |
| dc.subject (關鍵詞) | maximum a posteriori | en_US |
| dc.subject (關鍵詞) | Laplace approximation | en_US |
| dc.subject (關鍵詞) | regularization | en_US |
| dc.title (題名) | 結合層級貝氏模型與後驗眾數於顧客購買時間間隔之研究 | zh_TW |
| dc.title (題名) | A Study on Customer Interpurchase Times by Incorporating Hierarchical Bayesian Models with Posterior Mode Estimation | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 郭瑞祥、蔣明晃、陳薏棻、楊凱全，”應用層級被式理論於跨商品類別之顧客 購 買期間預測模型”，管理學報，2009 蔣宛蓉，”廣義伽馬分配於顧客購買時間模型之應用”，2020Allenby, G. M., Leone, R. P., and Jen, L. (1999). A dynamic model of purchase timing with application to direct marketing. Journal of the American Statistical Association, 94(446), pages 365-374.Cox, C., Chu, H., Schneider, M. F., and Munoz, A. (2007). Parametric survival analysis and taxonomy of hazard functions for the generalized gamma distribution. Statistics in Medicine, 26(23), pages 4352-4374.Dadpay, A.,Soofi, E. S., and Soyer, R.(2007). Information measures for generalized gamma family. Journal of Econometrics, 138(2), pages 568~585.Jiang, W. R., Chen, L . S., Weng, C. H .(2021). The Generalized Gamma Distribution with Application to the Modeling of Customer’s Purchase Times. Journal of the Chinese Statistical Association, 59(2021), 255-279.Stacy, E. W. (1962). A Generalization of the Gamma Distribution. The Annals of Mathematical Statistics, 33(3), 1187–1192. | zh_TW |
